A new nonparametric copula framework for the joint analysis of river temperature and low flow characteristics for aquatic habitat risk assessment
Bibliographic record
Abstract
The joint probability analysis of river water temperature (RWT) and low flow (LF) characteristics is essential as their combined effect can negatively affect aquatic species, e.g., ectotherm fish. Traditional multivariate models may not be as effective as copula-based methodologies. This study introduces a new multivariate approach, the nonparametric copula density framework, free from any distribution assumption in their univariate margins and copula joint density. The proposed framework utilized RWT and LF datasets collected at five different river stations in Switzerland. The study evaluates a nonparametric Gaussian kernel with six bandwidth selectors to model marginal densities. It employs nonparametric-based Beta kernel density, Bernstein estimator, and Transformation kernel estimator to approximate copula density with nonparametric and parametric margins. The performance of some parametric copula densities was also compared. The most justifiable models were employed to estimate bivariate joint exceedance probabilities and return periods (RPs). The Beta kernel copula with Gaussian kernel margins outperformed other models for most stations; Bernstein and Transformation copula with Gaussian kernel margins were better for only one station each. The univariate RPs (RWT or LF) are lower than the AND-joint but higher than OR joint case. As the percentile value of LF events (serve as a conditioning variable) increases, the bivariate joint RPs of RWT also increase. Higher values in RWT events result in higher RPs than lower values at the fixed percentile value of LF. All such estimated risk statistics are beneficial to analyze their mutual risk in aquatic habitats and freshwater ecosystems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".